code: def test_forward_works_with_projection_layer(self):
      vocab = Vocabulary()
      vocab.add_token_to_namespace("the")
      vocab.add_token_to_namespace("a")
      params = Params(
          {
              "pretrained_file": str(
                  self.FIXTURES_ROOT
                  / "embeddings/glove.6B.300d.sample.txt.gz"
              ),
              "embedding_dim": 300,
              "projection_dim": 20,
          }
      )
      embedding_layer = Embedding.from_params(params, vocab=vocab)
      input_tensor = torch.LongTensor([[3, 2, 1, 0]])
      embedded = embedding_layer(input_tensor).data.numpy()
      assert embedded.shape == (1, 4, 20)
  
      input_tensor = torch.LongTensor([[[3, 2, 1, 0]]])
      embedded = embedding_layer(input_tensor).data.numpy()
      assert embedded.shape == (1, 1, 4, 20)
  
imports: import gzip
  import warnings
  import numpy
  import pytest
  import torch
  from allennlp.common import Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.data import Vocabulary
  from allennlp.modules.token_embedders.embedding import _read_pretrained_embeddings_file, Embedding, EmbeddingsTextFile, format_embeddings_file_uri, parse_embeddings_file_uri
label_desc: Use image node_id 2 for calling the TestEmbedding obj's underlying member method code with example usage: obj.test_forward_works_with_projection_layer() without return types